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Measuring downside risk - realised semivariance

Citations

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Cited by:

  1. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
  2. Zhang, Hongwei & Jin, Chen & Bouri, Elie & Gao, Wang & Xu, Yahua, 2023. "Realized higher-order moments spillovers between commodity and stock markets: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 30(C).
  3. Chevallier, Julien & Sévi, Benoît, 2012. "On the volatility–volume relationship in energy futures markets using intraday data," Energy Economics, Elsevier, vol. 34(6), pages 1896-1909.
  4. Luo, Xin & Tao, Yunqing & Zou, Kai, 2022. "A new measure of realized volatility: Inertial and reverse realized semivariance," Finance Research Letters, Elsevier, vol. 47(PA).
  5. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2015. "Volatility transmission in global financial markets," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 3-18.
  6. Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
  7. Kislay Kumar Jha & Dirk G. Baur, 2020. "Regime-Dependent Good and Bad Volatility of Bitcoin," JRFM, MDPI, vol. 13(12), pages 1-16, December.
  8. Roh, Tai-Yong & Byun, Suk Joon & Xu, Yahua, 2020. "Downside uncertainty shocks in the oil and gold markets," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 291-307.
  9. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
  10. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
  11. Junjie Hu & Wolfgang Karl Hardle & Weiyu Kuo, 2019. "Risk of Bitcoin Market: Volatility, Jumps, and Forecasts," Papers 1912.05228, arXiv.org, revised Dec 2021.
  12. Wang Gao & Jiajia Wei & Shixiong Yang, 2023. "The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
  13. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
  14. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
  15. Srivastava, Pranjal & Jacob, Joshy, 2022. "Arbitrage constraints and behaviour of volatility components: Evidence from a natural experiment," IIMA Working Papers WP 2022-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
  16. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
  17. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
  18. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
  19. Si Mohammed, Kamel & Tedeschi, Marco & Mallek, Sabrine & Tarczyńska-Łuniewska, Małgorzata & Zhang, Anqi, 2023. "Realized semi variance quantile connectedness between oil prices and stock market: Spillover from Russian-Ukraine clash," Resources Policy, Elsevier, vol. 85(PA).
  20. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.
  21. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
  22. Glenn Kit Foong Ho & Sirimon Treepongkaruna & Marvin Wee & Chaiyuth Padungsaksawasdi, 2022. "The effect of short selling on volatility and jumps," Australian Journal of Management, Australian School of Business, vol. 47(1), pages 34-52, February.
  23. Bo Yu & Zhijia Chang, 2024. "Connectedness of Carbon Price and Energy Price under Shocks: A Study Based on Positive and Negative Price Volatility," Sustainability, MDPI, vol. 16(12), pages 1-26, June.
  24. Jozef Baruník & Matěj Nevrla, 2023. "Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1590-1646.
  25. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
  26. repec:dau:papers:123456789/6887 is not listed on IDEAS
  27. Apergis, Nicholas, 2023. "Realized higher-order moments spillovers across cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
  28. Wang, Ziwei & Li, Youwei & He, Feng, 2020. "Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China," Research in International Business and Finance, Elsevier, vol. 53(C).
  29. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
  30. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
  31. Da Fonseca, José & Xu, Yahua, 2017. "Higher moment risk premiums for the crude oil market: A downside and upside conditional decomposition," Energy Economics, Elsevier, vol. 67(C), pages 410-422.
  32. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
  33. Todorova, Neda & Clements, Adam E., 2018. "The volatility-volume relationship in the LME futures market for industrial metals," Resources Policy, Elsevier, vol. 58(C), pages 111-124.
  34. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
  35. Chen, Yufeng & Li, Wenqi & Qu, Fang, 2019. "Dynamic asymmetric spillovers and volatility interdependence on China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 825-838.
  36. Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," Working Papers hal-04140997, HAL.
  37. Lai T. Hoang & Dirk G. Baur, 2021. "Spillovers and Asset Allocation," JRFM, MDPI, vol. 14(8), pages 1-31, July.
  38. Giampiero Gallo & Ostap Okhrin & Giuseppe Storti, 2024. "Dynamic tail risk forecasting: what do realized skewness and kurtosis add?," Papers 2409.13516, arXiv.org.
  39. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany.
  40. Feng, Lingbing & Rao, Haicheng & Lucey, Brian & Zhu, Yiying, 2024. "Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1595-1615.
  41. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
  42. Peter, Eckley, 2015. "Measuring economic uncertainty using news-media textual data," MPRA Paper 64874, University Library of Munich, Germany, revised 01 May 2015.
  43. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
  44. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.
  45. He, Feng & Ma, Feng & Wang, Ziwei & Yang, Bohan, 2021. "Asymmetric volatility spillover between oil-importing and oil-exporting countries' economic policy uncertainty and China's energy sector," International Review of Financial Analysis, Elsevier, vol. 75(C).
  46. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).
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